Service matching systems increasingly use web and mobile applications to manage on-demand requests for transportation. Some on-demand service matching systems, for example, receive requests from persons through a mobile application requesting transportation from one geographic area to another geographic area. To fulfill such requests, on-demand service matching systems traditionally use a computational model that matches nearby transportation vehicles with requests from persons seeking transportation. By efficiently matching nearby transportation vehicles with requests, on-demand service matching systems can use a computational model to reduce an estimated time of arrival of a transportation vehicle to a requestor's location.
Some conventional on-demand service matching systems use static computational models to match requests with nearby providers of transportation vehicles, such as drivers. Static computational models often cannot efficiently match requests with providers during volatile or high-volume time periods of requests. In other words, existing static computational models lack the ability to adjust the computational logic that matches transportation vehicles with requestors—while maintaining reasonable estimated times of arrival—when requests reach high volumes or rapidly vary in volume.
Accordingly, conventional on-demand service matching systems may create problems for both people requesting transportation vehicles and drivers providing transportation vehicles. For example, conventional on-demand service matching systems may rigidly and too quickly dispatch transportation vehicles to requestors based on a static computational model without adjusting for an increase in request volumes or decrease in available transportation vehicles. By employing static computational models, on-demand service matching systems often too quickly dispatch transportation vehicles and leave some requestors with either an excessive estimated time of arrival or no available transportation vehicle to satisfy the request.